Overview

Dataset statistics

Number of variables29
Number of observations65510
Missing cells820728
Missing cells (%)43.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.5 MiB
Average record size in memory232.0 B

Variable types

DateTime1
Numeric12
Categorical12
Text1
Unsupported3

Alerts

tR is highly imbalanced (99.9%)Imbalance
Pa has 23558 (36.0%) missing valuesMissing
ff10 has 65314 (99.7%) missing valuesMissing
ff3 has 64071 (97.8%) missing valuesMissing
N has 3726 (5.7%) missing valuesMissing
W1 has 35176 (53.7%) missing valuesMissing
W2 has 35176 (53.7%) missing valuesMissing
Tn has 54267 (82.8%) missing valuesMissing
Tx has 58512 (89.3%) missing valuesMissing
Cl has 16143 (24.6%) missing valuesMissing
Nh has 13107 (20.0%) missing valuesMissing
H has 13086 (20.0%) missing valuesMissing
Cm has 33715 (51.5%) missing valuesMissing
Ch has 37018 (56.5%) missing valuesMissing
VV has 25905 (39.5%) missing valuesMissing
RRR has 44894 (68.5%) missing valuesMissing
tR has 44893 (68.5%) missing valuesMissing
E has 62595 (95.6%) missing valuesMissing
Tg has 63212 (96.5%) missing valuesMissing
E' has 62818 (95.9%) missing valuesMissing
sss has 63042 (96.2%) missing valuesMissing
Local time in Moscow has unique valuesUnique
VV is an unsupported type, check if it needs cleaning or further analysisUnsupported
RRR is an unsupported type, check if it needs cleaning or further analysisUnsupported
sss is an unsupported type, check if it needs cleaning or further analysisUnsupported
Pa has 2739 (4.2%) zerosZeros
Ff has 16859 (25.7%) zerosZeros

Reproduction

Analysis started2024-06-23 15:18:21.965541
Analysis finished2024-06-23 15:19:35.925544
Duration1 minute and 13.96 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Distinct65510
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size511.9 KiB
Minimum2005-01-02 03:00:00
Maximum2024-12-04 21:00:00
2024-06-23T18:19:36.158516image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:36.532820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

T
Real number (ℝ)

Distinct646
Distinct (%)1.0%
Missing7
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean6.6552524
Minimum-30.5
Maximum37.8
Zeros256
Zeros (%)0.4%
Negative18286
Negative (%)27.9%
Memory size511.9 KiB
2024-06-23T18:19:36.939193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-30.5
5-th percentile-11.2
Q1-0.8
median6.1
Q315.1
95-th percentile23.8
Maximum37.8
Range68.3
Interquartile range (IQR)15.9

Descriptive statistics

Standard deviation10.786456
Coefficient of variation (CV)1.6207434
Kurtosis-0.48078284
Mean6.6552524
Median Absolute Deviation (MAD)7.9
Skewness-0.093031673
Sum435939
Variance116.34764
MonotonicityNot monotonic
2024-06-23T18:19:37.248194image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 365
 
0.6%
1.3 364
 
0.6%
0.5 327
 
0.5%
0.3 325
 
0.5%
1 324
 
0.5%
0.6 323
 
0.5%
1.7 321
 
0.5%
0.4 320
 
0.5%
1.6 317
 
0.5%
1.5 315
 
0.5%
Other values (636) 62202
95.0%
ValueCountFrequency (%)
-30.5 1
< 0.1%
-30.4 1
< 0.1%
-30.2 2
< 0.1%
-30 1
< 0.1%
-29.9 2
< 0.1%
-29.7 1
< 0.1%
-29.6 1
< 0.1%
-29.4 1
< 0.1%
-29.3 1
< 0.1%
-29 1
< 0.1%
ValueCountFrequency (%)
37.8 1
 
< 0.1%
37.3 1
 
< 0.1%
37.2 1
 
< 0.1%
36.6 1
 
< 0.1%
36.3 1
 
< 0.1%
36.2 1
 
< 0.1%
35.8 4
< 0.1%
35.4 3
< 0.1%
35.3 1
 
< 0.1%
35.2 3
< 0.1%

Po
Real number (ℝ)

Distinct606
Distinct (%)0.9%
Missing176
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean747.6735
Minimum712.2
Maximum778.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size511.9 KiB
2024-06-23T18:19:37.548842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum712.2
5-th percentile734.9
Q1743
median747.7
Q3752.4
95-th percentile760.4
Maximum778.2
Range66
Interquartile range (IQR)9.4

Descriptive statistics

Standard deviation7.7037441
Coefficient of variation (CV)0.010303621
Kurtosis0.80323385
Mean747.6735
Median Absolute Deviation (MAD)4.7
Skewness-0.031449739
Sum48848500
Variance59.347674
MonotonicityNot monotonic
2024-06-23T18:19:37.855871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
746.8 601
 
0.9%
747.4 598
 
0.9%
748 596
 
0.9%
750.4 578
 
0.9%
749.2 573
 
0.9%
748.6 567
 
0.9%
745.6 542
 
0.8%
746.2 536
 
0.8%
749.8 515
 
0.8%
744.4 502
 
0.8%
Other values (596) 59726
91.2%
ValueCountFrequency (%)
712.2 1
< 0.1%
712.8 1
< 0.1%
713.3 1
< 0.1%
713.4 2
< 0.1%
713.8 2
< 0.1%
714.6 1
< 0.1%
714.7 1
< 0.1%
714.8 1
< 0.1%
715.2 2
< 0.1%
715.3 2
< 0.1%
ValueCountFrequency (%)
778.2 1
 
< 0.1%
778 1
 
< 0.1%
777.9 1
 
< 0.1%
777.7 2
< 0.1%
777.6 1
 
< 0.1%
777.4 1
 
< 0.1%
777.1 1
 
< 0.1%
776.9 1
 
< 0.1%
776.6 1
 
< 0.1%
776.5 3
< 0.1%

P
Real number (ℝ)

Distinct628
Distinct (%)1.0%
Missing137
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean762.04788
Minimum725.9
Maximum794.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size511.9 KiB
2024-06-23T18:19:38.168871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum725.9
5-th percentile749.1
Q1757.2
median761.8
Q3766.8
95-th percentile775.6
Maximum794.5
Range68.6
Interquartile range (IQR)9.6

Descriptive statistics

Standard deviation7.9675075
Coefficient of variation (CV)0.01045539
Kurtosis0.81431743
Mean762.04788
Median Absolute Deviation (MAD)4.8
Skewness0.080122903
Sum49817356
Variance63.481176
MonotonicityNot monotonic
2024-06-23T18:19:38.499412image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
760.6 589
 
0.9%
763.6 585
 
0.9%
761.2 561
 
0.9%
760 549
 
0.8%
763 542
 
0.8%
761.8 533
 
0.8%
759.4 528
 
0.8%
762.4 527
 
0.8%
764.2 499
 
0.8%
758.8 489
 
0.7%
Other values (618) 59971
91.5%
ValueCountFrequency (%)
725.9 2
< 0.1%
727 2
< 0.1%
727.4 1
< 0.1%
727.6 1
< 0.1%
727.8 1
< 0.1%
727.9 1
< 0.1%
728.6 1
< 0.1%
728.7 2
< 0.1%
729.3 2
< 0.1%
729.4 2
< 0.1%
ValueCountFrequency (%)
794.5 1
 
< 0.1%
794.2 1
 
< 0.1%
794 2
 
< 0.1%
793.8 1
 
< 0.1%
793.7 1
 
< 0.1%
793.6 1
 
< 0.1%
793.4 1
 
< 0.1%
793.2 2
 
< 0.1%
793.1 1
 
< 0.1%
793 8
< 0.1%

Pa
Real number (ℝ)

MISSING  ZEROS 

Distinct123
Distinct (%)0.3%
Missing23558
Missing (%)36.0%
Infinite0
Infinite (%)0.0%
Mean-0.0019403127
Minimum-13.4
Maximum9.9
Zeros2739
Zeros (%)4.2%
Negative19259
Negative (%)29.4%
Memory size511.9 KiB
2024-06-23T18:19:38.993440image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-13.4
5-th percentile-1.4
Q1-0.5
median0
Q30.5
95-th percentile1.4
Maximum9.9
Range23.3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.90889337
Coefficient of variation (CV)-468.42622
Kurtosis5.3305839
Mean-0.0019403127
Median Absolute Deviation (MAD)0.5
Skewness-0.30472254
Sum-81.4
Variance0.82608715
MonotonicityNot monotonic
2024-06-23T18:19:39.295119image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2739
 
4.2%
0.3 2432
 
3.7%
-0.3 2256
 
3.4%
0.1 2176
 
3.3%
0.2 2165
 
3.3%
-0.1 2113
 
3.2%
-0.2 2020
 
3.1%
0.6 1733
 
2.6%
-0.4 1726
 
2.6%
0.4 1683
 
2.6%
Other values (113) 20909
31.9%
(Missing) 23558
36.0%
ValueCountFrequency (%)
-13.4 1
< 0.1%
-8.5 1
< 0.1%
-8 2
< 0.1%
-7.8 1
< 0.1%
-7.4 1
< 0.1%
-7.1 1
< 0.1%
-6.9 2
< 0.1%
-6.5 1
< 0.1%
-6.2 1
< 0.1%
-5.8 1
< 0.1%
ValueCountFrequency (%)
9.9 1
< 0.1%
9.6 1
< 0.1%
8.6 1
< 0.1%
6.8 1
< 0.1%
5.9 2
< 0.1%
5.6 1
< 0.1%
5.4 1
< 0.1%
5.1 1
< 0.1%
5 1
< 0.1%
4.9 1
< 0.1%

U
Real number (ℝ)

Distinct88
Distinct (%)0.1%
Missing44
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean75.39393
Minimum12
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size511.9 KiB
2024-06-23T18:19:39.595815image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile38
Q164
median81
Q390
95-th percentile97
Maximum100
Range88
Interquartile range (IQR)26

Descriptive statistics

Standard deviation18.483595
Coefficient of variation (CV)0.24516026
Kurtosis-0.070879659
Mean75.39393
Median Absolute Deviation (MAD)11
Skewness-0.87881952
Sum4935739
Variance341.64329
MonotonicityNot monotonic
2024-06-23T18:19:39.888465image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 2254
 
3.4%
85 2134
 
3.3%
94 2002
 
3.1%
86 1887
 
2.9%
88 1861
 
2.8%
89 1823
 
2.8%
96 1818
 
2.8%
91 1818
 
2.8%
80 1743
 
2.7%
92 1741
 
2.7%
Other values (78) 46385
70.8%
ValueCountFrequency (%)
12 1
 
< 0.1%
14 5
 
< 0.1%
15 4
 
< 0.1%
16 8
 
< 0.1%
17 9
 
< 0.1%
18 25
 
< 0.1%
19 20
 
< 0.1%
20 40
0.1%
21 39
0.1%
22 78
0.1%
ValueCountFrequency (%)
100 668
 
1.0%
99 1071
1.6%
98 1014
1.5%
97 1588
2.4%
96 1818
2.8%
95 1633
2.5%
94 2002
3.1%
93 1695
2.6%
92 1741
2.7%
91 1818
2.8%

DD
Categorical

Distinct18
Distinct (%)< 0.1%
Missing50
Missing (%)0.1%
Memory size511.9 KiB
Calm, no wind
16859 
Wind blowing from the west
4842 
Wind blowing from the south-southeast
4448 
Wind blowing from the north
4307 
Wind blowing from the south
3777 
Other values (13)
31227 

Length

Max length37
Median length32
Mean length27.410067
Min length13

Characters and Unicode

Total characters1794263
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCalm, no wind
2nd rowWind blowing from the south-southeast
3rd rowWind blowing from the south-east
4th rowWind blowing from the south-southeast
5th rowWind blowing from the south-southeast

Common Values

ValueCountFrequency (%)
Calm, no wind 16859
25.7%
Wind blowing from the west 4842
 
7.4%
Wind blowing from the south-southeast 4448
 
6.8%
Wind blowing from the north 4307
 
6.6%
Wind blowing from the south 3777
 
5.8%
Wind blowing from the west-southwest 3594
 
5.5%
Wind blowing from the south-east 3589
 
5.5%
Wind blowing from the north-northwest 3466
 
5.3%
Wind blowing from the north-west 3343
 
5.1%
Wind blowing from the south-west 3086
 
4.7%
Other values (8) 14149
21.6%

Length

2024-06-23T18:19:40.163468image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
wind 65460
22.3%
blowing 48600
16.6%
from 48600
16.6%
the 48600
16.6%
calm 16859
 
5.7%
no 16859
 
5.7%
west 4842
 
1.6%
south-southeast 4448
 
1.5%
north 4307
 
1.5%
south 3777
 
1.3%
Other values (14) 31228
10.6%

Most occurring characters

ValueCountFrequency (%)
228120
12.7%
o 168269
 
9.4%
n 156285
 
8.7%
t 153056
 
8.5%
i 114063
 
6.4%
h 102809
 
5.7%
e 98848
 
5.5%
w 95400
 
5.3%
s 79090
 
4.4%
r 73967
 
4.1%
Other values (14) 524356
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1794263
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
228120
12.7%
o 168269
 
9.4%
n 156285
 
8.7%
t 153056
 
8.5%
i 114063
 
6.4%
h 102809
 
5.7%
e 98848
 
5.5%
w 95400
 
5.3%
s 79090
 
4.4%
r 73967
 
4.1%
Other values (14) 524356
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1794263
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
228120
12.7%
o 168269
 
9.4%
n 156285
 
8.7%
t 153056
 
8.5%
i 114063
 
6.4%
h 102809
 
5.7%
e 98848
 
5.5%
w 95400
 
5.3%
s 79090
 
4.4%
r 73967
 
4.1%
Other values (14) 524356
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1794263
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
228120
12.7%
o 168269
 
9.4%
n 156285
 
8.7%
t 153056
 
8.5%
i 114063
 
6.4%
h 102809
 
5.7%
e 98848
 
5.5%
w 95400
 
5.3%
s 79090
 
4.4%
r 73967
 
4.1%
Other values (14) 524356
29.2%

Ff
Real number (ℝ)

ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing50
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.1644974
Minimum0
Maximum8
Zeros16859
Zeros (%)25.7%
Negative0
Negative (%)0.0%
Memory size511.9 KiB
2024-06-23T18:19:40.409583image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation0.94550237
Coefficient of variation (CV)0.8119403
Kurtosis0.4976303
Mean1.1644974
Median Absolute Deviation (MAD)1
Skewness0.69393788
Sum76228
Variance0.89397473
MonotonicityNot monotonic
2024-06-23T18:19:40.671170image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 27536
42.0%
0 16859
25.7%
2 15652
23.9%
3 4429
 
6.8%
4 839
 
1.3%
5 130
 
0.2%
6 11
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
(Missing) 50
 
0.1%
ValueCountFrequency (%)
0 16859
25.7%
1 27536
42.0%
2 15652
23.9%
3 4429
 
6.8%
4 839
 
1.3%
5 130
 
0.2%
6 11
 
< 0.1%
7 3
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 3
 
< 0.1%
6 11
 
< 0.1%
5 130
 
0.2%
4 839
 
1.3%
3 4429
 
6.8%
2 15652
23.9%
1 27536
42.0%
0 16859
25.7%

ff10
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)4.1%
Missing65314
Missing (%)99.7%
Infinite0
Infinite (%)0.0%
Mean11.168367
Minimum10
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size511.9 KiB
2024-06-23T18:19:40.947169image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median11
Q312
95-th percentile13.25
Maximum18
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3689672
Coefficient of variation (CV)0.12257541
Kurtosis3.9832576
Mean11.168367
Median Absolute Deviation (MAD)1
Skewness1.6791574
Sum2189
Variance1.8740712
MonotonicityNot monotonic
2024-06-23T18:19:41.194203image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
10 79
 
0.1%
11 53
 
0.1%
12 38
 
0.1%
13 16
 
< 0.1%
15 4
 
< 0.1%
14 3
 
< 0.1%
16 2
 
< 0.1%
18 1
 
< 0.1%
(Missing) 65314
99.7%
ValueCountFrequency (%)
10 79
0.1%
11 53
0.1%
12 38
0.1%
13 16
 
< 0.1%
14 3
 
< 0.1%
15 4
 
< 0.1%
16 2
 
< 0.1%
18 1
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 2
 
< 0.1%
15 4
 
< 0.1%
14 3
 
< 0.1%
13 16
 
< 0.1%
12 38
0.1%
11 53
0.1%
10 79
0.1%

ff3
Real number (ℝ)

MISSING 

Distinct12
Distinct (%)0.8%
Missing64071
Missing (%)97.8%
Infinite0
Infinite (%)0.0%
Mean11.448923
Minimum10
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size511.9 KiB
2024-06-23T18:19:41.432836image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile10
Q110
median11
Q312
95-th percentile14
Maximum21
Range11
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5390142
Coefficient of variation (CV)0.13442436
Kurtosis3.3621821
Mean11.448923
Median Absolute Deviation (MAD)1
Skewness1.4481978
Sum16475
Variance2.3685646
MonotonicityNot monotonic
2024-06-23T18:19:41.661687image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
10 516
 
0.8%
12 366
 
0.6%
11 284
 
0.4%
13 144
 
0.2%
14 67
 
0.1%
15 34
 
0.1%
16 12
 
< 0.1%
17 7
 
< 0.1%
18 6
 
< 0.1%
19 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 64071
97.8%
ValueCountFrequency (%)
10 516
0.8%
11 284
0.4%
12 366
0.6%
13 144
 
0.2%
14 67
 
0.1%
15 34
 
0.1%
16 12
 
< 0.1%
17 7
 
< 0.1%
18 6
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
21 1
 
< 0.1%
20 1
 
< 0.1%
19 1
 
< 0.1%
18 6
 
< 0.1%
17 7
 
< 0.1%
16 12
 
< 0.1%
15 34
 
0.1%
14 67
 
0.1%
13 144
 
0.2%
12 366
0.6%

N
Categorical

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing3726
Missing (%)5.7%
Memory size511.9 KiB
100%.
27913 
no clouds
9297 
90 or more, but not 100%
6914 
70 – 80%.
6685 
20–30%.
3724 
Other values (5)
7251 

Length

Max length58
Median length25
Mean length8.6160171
Min length4

Characters and Unicode

Total characters532332
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row70 – 80%.
2nd row70 – 80%.
3rd row90 or more, but not 100%
4th row100%.
5th row100%.

Common Values

ValueCountFrequency (%)
100%. 27913
42.6%
no clouds 9297
 
14.2%
90 or more, but not 100% 6914
 
10.6%
70 – 80%. 6685
 
10.2%
20–30%. 3724
 
5.7%
60%. 2942
 
4.5%
40%. 2130
 
3.3%
50%. 1206
 
1.8%
10% or less, but not 0 901
 
1.4%
Sky obscured by fog and/or other meteorological phenomena. 72
 
0.1%
(Missing) 3726
 
5.7%

Length

2024-06-23T18:19:41.915686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T18:19:42.210471image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100 34827
28.1%
no 9297
 
7.5%
clouds 9297
 
7.5%
or 7815
 
6.3%
but 7815
 
6.3%
not 7815
 
6.3%
90 6914
 
5.6%
more 6914
 
5.6%
70 6685
 
5.4%
– 6685
 
5.4%
Other values (16) 19966
16.1%

Most occurring characters

ValueCountFrequency (%)
0 105466
19.8%
70061
13.2%
% 52415
9.8%
. 44672
 
8.4%
o 41714
 
7.8%
1 35728
 
6.7%
n 17328
 
3.3%
u 17184
 
3.2%
t 15774
 
3.0%
r 15017
 
2.8%
Other values (27) 116973
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 532332
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 105466
19.8%
70061
13.2%
% 52415
9.8%
. 44672
 
8.4%
o 41714
 
7.8%
1 35728
 
6.7%
n 17328
 
3.3%
u 17184
 
3.2%
t 15774
 
3.0%
r 15017
 
2.8%
Other values (27) 116973
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 532332
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 105466
19.8%
70061
13.2%
% 52415
9.8%
. 44672
 
8.4%
o 41714
 
7.8%
1 35728
 
6.7%
n 17328
 
3.3%
u 17184
 
3.2%
t 15774
 
3.0%
r 15017
 
2.8%
Other values (27) 116973
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 532332
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 105466
19.8%
70061
13.2%
% 52415
9.8%
. 44672
 
8.4%
o 41714
 
7.8%
1 35728
 
6.7%
n 17328
 
3.3%
u 17184
 
3.2%
t 15774
 
3.0%
r 15017
 
2.8%
Other values (27) 116973
22.0%

WW
Text

Distinct133
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size511.9 KiB
2024-06-23T18:19:42.641108image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length140
Median length1
Mean length12.879148
Min length1

Characters and Unicode

Total characters843713
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row
ValueCountFrequency (%)
of 15037
 
12.2%
mist 14543
 
11.8%
slight 8032
 
6.5%
at 6192
 
5.0%
time 6187
 
5.0%
observation 6187
 
5.0%
rain 5784
 
4.7%
shower(s 5686
 
4.6%
continuous 5537
 
4.5%
fall 4039
 
3.3%
Other values (117) 46297
37.5%
2024-06-23T18:19:43.316528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
158698
18.8%
o 68570
 
8.1%
t 64006
 
7.6%
s 60664
 
7.2%
i 55849
 
6.6%
n 50451
 
6.0%
e 49278
 
5.8%
a 38603
 
4.6%
l 30932
 
3.7%
. 30480
 
3.6%
Other values (41) 236182
28.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 843713
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
158698
18.8%
o 68570
 
8.1%
t 64006
 
7.6%
s 60664
 
7.2%
i 55849
 
6.6%
n 50451
 
6.0%
e 49278
 
5.8%
a 38603
 
4.6%
l 30932
 
3.7%
. 30480
 
3.6%
Other values (41) 236182
28.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 843713
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
158698
18.8%
o 68570
 
8.1%
t 64006
 
7.6%
s 60664
 
7.2%
i 55849
 
6.6%
n 50451
 
6.0%
e 49278
 
5.8%
a 38603
 
4.6%
l 30932
 
3.7%
. 30480
 
3.6%
Other values (41) 236182
28.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 843713
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
158698
18.8%
o 68570
 
8.1%
t 64006
 
7.6%
s 60664
 
7.2%
i 55849
 
6.6%
n 50451
 
6.0%
e 49278
 
5.8%
a 38603
 
4.6%
l 30932
 
3.7%
. 30480
 
3.6%
Other values (41) 236182
28.0%

W1
Categorical

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing35176
Missing (%)53.7%
Memory size511.9 KiB
Shower(s).
7289 
Cloud covering more than 1/2 of the sky throughout the appropriate period.
6954 
Snow, or rain and snow mixed.
6468 
Rain.
2885 
Cloud covering 1/2 or less of the sky throughout the appropriate period.
2690 
Other values (5)
4048 

Length

Max length129
Median length72
Mean length44.709798
Min length5

Characters and Unicode

Total characters1356227
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowShower(s).
2nd rowShower(s).
3rd rowShower(s).
4th rowCloud covering more than 1/2 of the sky during part of the appropriate period and covering 1/2 or less during part of the period.
5th rowShower(s).

Common Values

ValueCountFrequency (%)
Shower(s). 7289
 
11.1%
Cloud covering more than 1/2 of the sky throughout the appropriate period. 6954
 
10.6%
Snow, or rain and snow mixed. 6468
 
9.9%
Rain. 2885
 
4.4%
Cloud covering 1/2 or less of the sky throughout the appropriate period. 2690
 
4.1%
Cloud covering more than 1/2 of the sky during part of the appropriate period and covering 1/2 or less during part of the period. 2474
 
3.8%
Thunderstorm(s) with or without precipitation. 837
 
1.3%
Drizzle. 353
 
0.5%
Fog or ice fog or thick haze. 202
 
0.3%
Sandstorm, duststorm or blowing snow. 182
 
0.3%
(Missing) 35176
53.7%

Length

2024-06-23T18:19:43.626885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T18:19:43.908030image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
the 26710
 
11.6%
of 17066
 
7.4%
covering 14592
 
6.3%
1/2 14592
 
6.3%
period 14592
 
6.3%
snow 13118
 
5.7%
or 13055
 
5.7%
cloud 12118
 
5.2%
sky 12118
 
5.2%
appropriate 12118
 
5.2%
Other values (22) 80869
35.0%

Most occurring characters

ValueCountFrequency (%)
200614
14.8%
o 136125
 
10.0%
r 112428
 
8.3%
e 98792
 
7.3%
t 78262
 
5.8%
i 67195
 
5.0%
h 65630
 
4.8%
n 62419
 
4.6%
a 58128
 
4.3%
p 57568
 
4.2%
Other values (28) 419066
30.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1356227
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
200614
14.8%
o 136125
 
10.0%
r 112428
 
8.3%
e 98792
 
7.3%
t 78262
 
5.8%
i 67195
 
5.0%
h 65630
 
4.8%
n 62419
 
4.6%
a 58128
 
4.3%
p 57568
 
4.2%
Other values (28) 419066
30.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1356227
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
200614
14.8%
o 136125
 
10.0%
r 112428
 
8.3%
e 98792
 
7.3%
t 78262
 
5.8%
i 67195
 
5.0%
h 65630
 
4.8%
n 62419
 
4.6%
a 58128
 
4.3%
p 57568
 
4.2%
Other values (28) 419066
30.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1356227
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
200614
14.8%
o 136125
 
10.0%
r 112428
 
8.3%
e 98792
 
7.3%
t 78262
 
5.8%
i 67195
 
5.0%
h 65630
 
4.8%
n 62419
 
4.6%
a 58128
 
4.3%
p 57568
 
4.2%
Other values (28) 419066
30.9%

W2
Categorical

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing35176
Missing (%)53.7%
Memory size511.9 KiB
Cloud covering more than 1/2 of the sky throughout the appropriate period.
21002 
Cloud covering more than 1/2 of the sky during part of the appropriate period and covering 1/2 or less during part of the period.
3695 
Cloud covering 1/2 or less of the sky throughout the appropriate period.
2797 
Shower(s).
 
731
Rain.
 
714
Other values (4)
 
1395

Length

Max length129
Median length74
Mean length75.318982
Min length5

Characters and Unicode

Total characters2284726
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCloud covering more than 1/2 of the sky throughout the appropriate period.
2nd rowCloud covering more than 1/2 of the sky throughout the appropriate period.
3rd rowCloud covering more than 1/2 of the sky throughout the appropriate period.
4th rowCloud covering more than 1/2 of the sky during part of the appropriate period and covering 1/2 or less during part of the period.
5th rowCloud covering more than 1/2 of the sky throughout the appropriate period.

Common Values

ValueCountFrequency (%)
Cloud covering more than 1/2 of the sky throughout the appropriate period. 21002
32.1%
Cloud covering more than 1/2 of the sky during part of the appropriate period and covering 1/2 or less during part of the period. 3695
 
5.6%
Cloud covering 1/2 or less of the sky throughout the appropriate period. 2797
 
4.3%
Shower(s). 731
 
1.1%
Rain. 714
 
1.1%
Sandstorm, duststorm or blowing snow. 665
 
1.0%
Snow, or rain and snow mixed. 466
 
0.7%
Drizzle. 196
 
0.3%
Fog or ice fog or thick haze. 68
 
0.1%
(Missing) 35176
53.7%

Length

2024-06-23T18:19:44.293216image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T18:19:44.561606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
the 58683
15.3%
of 34884
9.1%
covering 31189
8.2%
1/2 31189
8.2%
period 31189
8.2%
cloud 27494
7.2%
sky 27494
7.2%
appropriate 27494
7.2%
more 24697
 
6.5%
than 24697
 
6.5%
Other values (18) 63496
16.6%

Most occurring characters

ValueCountFrequency (%)
352172
15.4%
o 236763
 
10.4%
r 191124
 
8.4%
e 181273
 
7.9%
t 167925
 
7.3%
h 131845
 
5.8%
p 121061
 
5.3%
i 99905
 
4.4%
a 93149
 
4.1%
u 83147
 
3.6%
Other values (27) 626362
27.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2284726
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
352172
15.4%
o 236763
 
10.4%
r 191124
 
8.4%
e 181273
 
7.9%
t 167925
 
7.3%
h 131845
 
5.8%
p 121061
 
5.3%
i 99905
 
4.4%
a 93149
 
4.1%
u 83147
 
3.6%
Other values (27) 626362
27.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2284726
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
352172
15.4%
o 236763
 
10.4%
r 191124
 
8.4%
e 181273
 
7.9%
t 167925
 
7.3%
h 131845
 
5.8%
p 121061
 
5.3%
i 99905
 
4.4%
a 93149
 
4.1%
u 83147
 
3.6%
Other values (27) 626362
27.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2284726
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
352172
15.4%
o 236763
 
10.4%
r 191124
 
8.4%
e 181273
 
7.9%
t 167925
 
7.3%
h 131845
 
5.8%
p 121061
 
5.3%
i 99905
 
4.4%
a 93149
 
4.1%
u 83147
 
3.6%
Other values (27) 626362
27.4%

Tn
Real number (ℝ)

MISSING 

Distinct484
Distinct (%)4.3%
Missing54267
Missing (%)82.8%
Infinite0
Infinite (%)0.0%
Mean3.4133772
Minimum-30.8
Maximum28
Zeros45
Zeros (%)0.1%
Negative4008
Negative (%)6.1%
Memory size511.9 KiB
2024-06-23T18:19:44.919000image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-30.8
5-th percentile-13.5
Q1-2.5
median3.4
Q311
95-th percentile17.1
Maximum28
Range58.8
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation9.3547419
Coefficient of variation (CV)2.7406118
Kurtosis-0.20667358
Mean3.4133772
Median Absolute Deviation (MAD)6.8
Skewness-0.43098883
Sum38376.6
Variance87.511196
MonotonicityNot monotonic
2024-06-23T18:19:45.262553image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 74
 
0.1%
8.1 71
 
0.1%
0.8 69
 
0.1%
1.2 67
 
0.1%
0.7 67
 
0.1%
0.6 64
 
0.1%
-1.6 64
 
0.1%
-0.2 63
 
0.1%
-1.7 62
 
0.1%
12.6 58
 
0.1%
Other values (474) 10584
 
16.2%
(Missing) 54267
82.8%
ValueCountFrequency (%)
-30.8 1
< 0.1%
-30.4 1
< 0.1%
-29.9 1
< 0.1%
-29.8 1
< 0.1%
-29.4 1
< 0.1%
-28.6 1
< 0.1%
-28.5 1
< 0.1%
-28.3 1
< 0.1%
-27.7 2
< 0.1%
-27 1
< 0.1%
ValueCountFrequency (%)
28 1
< 0.1%
26 1
< 0.1%
24.6 1
< 0.1%
24.4 1
< 0.1%
24.1 1
< 0.1%
23.5 1
< 0.1%
22.6 1
< 0.1%
22.5 1
< 0.1%
22.2 1
< 0.1%
22.1 1
< 0.1%

Tx
Real number (ℝ)

MISSING 

Distinct550
Distinct (%)7.9%
Missing58512
Missing (%)89.3%
Infinite0
Infinite (%)0.0%
Mean10.213132
Minimum-70.6
Maximum38.2
Zeros21
Zeros (%)< 0.1%
Negative1493
Negative (%)2.3%
Memory size511.9 KiB
2024-06-23T18:19:45.578832image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-70.6
5-th percentile-8.6
Q11
median9.8
Q320.675
95-th percentile28.4
Maximum38.2
Range108.8
Interquartile range (IQR)19.675

Descriptive statistics

Standard deviation11.905568
Coefficient of variation (CV)1.1657117
Kurtosis-0.61589583
Mean10.213132
Median Absolute Deviation (MAD)9.7
Skewness-0.12029502
Sum71471.5
Variance141.74255
MonotonicityNot monotonic
2024-06-23T18:19:45.893439image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.2 41
 
0.1%
24 36
 
0.1%
2.6 35
 
0.1%
2 34
 
0.1%
1.2 34
 
0.1%
1.9 34
 
0.1%
-1.5 34
 
0.1%
23.5 33
 
0.1%
0.9 33
 
0.1%
2.5 32
 
< 0.1%
Other values (540) 6652
 
10.2%
(Missing) 58512
89.3%
ValueCountFrequency (%)
-70.6 1
< 0.1%
-26.9 1
< 0.1%
-25.4 1
< 0.1%
-25 1
< 0.1%
-23.5 1
< 0.1%
-22.9 2
< 0.1%
-22.7 1
< 0.1%
-22.3 1
< 0.1%
-21.7 1
< 0.1%
-21.5 2
< 0.1%
ValueCountFrequency (%)
38.2 1
< 0.1%
37.5 2
< 0.1%
37.3 1
< 0.1%
37.2 1
< 0.1%
36.9 1
< 0.1%
36.7 1
< 0.1%
36.6 1
< 0.1%
36.3 1
< 0.1%
36.1 1
< 0.1%
36 1
< 0.1%

Cl
Categorical

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing16143
Missing (%)24.6%
Memory size511.9 KiB
Stratocumulus other than Stratocumulus cumulogenitus.
18600 
Stratus fractus or Cumulus fractus of bad weather, or both (pannus), usually below Altostratus or Nimbostratus.
7204 
No Stratocumulus, Stratus, Cumulus or Cumulonimbus.
6940 
Stratocumulus cumulogenitus.
3626 
Cumulus and Stratocumulus other than Stratocumulus cumulogenitus, with bases at different levels.
3316 
Other values (5)
9681 

Length

Max length145
Median length126
Mean length70.640002
Min length28

Characters and Unicode

Total characters3487285
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowStratocumulus cumulogenitus.
2nd rowCumulus humilis or Cumulus fractus other than of bad weather, or both.
3rd rowCumulus mediocris or congestus, with or without Cumulus of species fractus or humilis or Stratocumulus, all having their bases at the same level.
4th rowStratocumulus other than Stratocumulus cumulogenitus.
5th rowNo Stratocumulus, Stratus, Cumulus or Cumulonimbus.

Common Values

ValueCountFrequency (%)
Stratocumulus other than Stratocumulus cumulogenitus. 18600
28.4%
Stratus fractus or Cumulus fractus of bad weather, or both (pannus), usually below Altostratus or Nimbostratus. 7204
 
11.0%
No Stratocumulus, Stratus, Cumulus or Cumulonimbus. 6940
 
10.6%
Stratocumulus cumulogenitus. 3626
 
5.5%
Cumulus and Stratocumulus other than Stratocumulus cumulogenitus, with bases at different levels. 3316
 
5.1%
Stratus nebulosus or Stratus fractus other than of bad weather, or both. 3109
 
4.7%
Cumulonimbus capillatus (often with an anvil), with or without Cumulonimbus calvus, Cumulus, Stratocumulus, Stratus or pannus. 2099
 
3.2%
Cumulus humilis or Cumulus fractus other than of bad weather, or both. 1878
 
2.9%
Cumulus mediocris or congestus, with or without Cumulus of species fractus or humilis or Stratocumulus, all having their bases at the same level. 1631
 
2.5%
Cumulonimbus calvus, with or without Cumulus, Stratocumulus or Stratus. 964
 
1.5%
(Missing) 16143
24.6%

Length

2024-06-23T18:19:46.348673image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T18:19:46.627904image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
stratocumulus 59092
 
13.5%
or 51176
 
11.7%
cumulus 27541
 
6.3%
other 26903
 
6.2%
than 26903
 
6.2%
cumulogenitus 25542
 
5.9%
stratus 23425
 
5.4%
fractus 21026
 
4.8%
of 13822
 
3.2%
both 12191
 
2.8%
Other values (32) 148944
34.1%

Most occurring characters

ValueCountFrequency (%)
u 476615
13.7%
387198
11.1%
t 362661
10.4%
s 251936
 
7.2%
o 242544
 
7.0%
a 215305
 
6.2%
r 214799
 
6.2%
l 182227
 
5.2%
m 150354
 
4.3%
e 122129
 
3.5%
Other values (20) 881517
25.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3487285
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 476615
13.7%
387198
11.1%
t 362661
10.4%
s 251936
 
7.2%
o 242544
 
7.0%
a 215305
 
6.2%
r 214799
 
6.2%
l 182227
 
5.2%
m 150354
 
4.3%
e 122129
 
3.5%
Other values (20) 881517
25.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3487285
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 476615
13.7%
387198
11.1%
t 362661
10.4%
s 251936
 
7.2%
o 242544
 
7.0%
a 215305
 
6.2%
r 214799
 
6.2%
l 182227
 
5.2%
m 150354
 
4.3%
e 122129
 
3.5%
Other values (20) 881517
25.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3487285
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 476615
13.7%
387198
11.1%
t 362661
10.4%
s 251936
 
7.2%
o 242544
 
7.0%
a 215305
 
6.2%
r 214799
 
6.2%
l 182227
 
5.2%
m 150354
 
4.3%
e 122129
 
3.5%
Other values (20) 881517
25.3%

Nh
Categorical

MISSING 

Distinct9
Distinct (%)< 0.1%
Missing13107
Missing (%)20.0%
Memory size511.9 KiB
100%.
24303 
70 – 80%.
5459 
90 or more, but not 100%
4817 
no clouds
4674 
20–30%.
4391 
Other values (4)
8759 

Length

Max length25
Median length23
Mean length8.044902
Min length4

Characters and Unicode

Total characters421577
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20–30%.
2nd row40%.
3rd row60%.
4th row60%.
5th rowno clouds

Common Values

ValueCountFrequency (%)
100%. 24303
37.1%
70 – 80%. 5459
 
8.3%
90 or more, but not 100% 4817
 
7.4%
no clouds 4674
 
7.1%
20–30%. 4391
 
6.7%
60%. 3129
 
4.8%
40%. 2649
 
4.0%
50%. 1788
 
2.7%
10% or less, but not 0 1193
 
1.8%
(Missing) 13107
20.0%

Length

2024-06-23T18:19:47.038875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T18:19:47.294478image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
100 29120
29.7%
or 6010
 
6.1%
but 6010
 
6.1%
not 6010
 
6.1%
– 5459
 
5.6%
80 5459
 
5.6%
70 5459
 
5.6%
90 4817
 
4.9%
more 4817
 
4.9%
clouds 4674
 
4.8%
Other values (8) 20210
20.6%

Most occurring characters

ValueCountFrequency (%)
0 92709
22.0%
51652
12.3%
% 47729
11.3%
. 41719
9.9%
1 30313
 
7.2%
o 26185
 
6.2%
t 12020
 
2.9%
r 10827
 
2.6%
n 10684
 
2.5%
u 10684
 
2.5%
Other values (17) 87055
20.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 421577
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 92709
22.0%
51652
12.3%
% 47729
11.3%
. 41719
9.9%
1 30313
 
7.2%
o 26185
 
6.2%
t 12020
 
2.9%
r 10827
 
2.6%
n 10684
 
2.5%
u 10684
 
2.5%
Other values (17) 87055
20.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 421577
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 92709
22.0%
51652
12.3%
% 47729
11.3%
. 41719
9.9%
1 30313
 
7.2%
o 26185
 
6.2%
t 12020
 
2.9%
r 10827
 
2.6%
n 10684
 
2.5%
u 10684
 
2.5%
Other values (17) 87055
20.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 421577
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 92709
22.0%
51652
12.3%
% 47729
11.3%
. 41719
9.9%
1 30313
 
7.2%
o 26185
 
6.2%
t 12020
 
2.9%
r 10827
 
2.6%
n 10684
 
2.5%
u 10684
 
2.5%
Other values (17) 87055
20.6%

H
Categorical

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing13086
Missing (%)20.0%
Memory size511.9 KiB
600-1000
22645 
2500 or more, or no clouds.
9942 
300-600
8941 
1000-1500
6162 
200-300
3547 
Other values (5)
 
1187

Length

Max length27
Median length13
Mean length11.475927
Min length6

Characters and Unicode

Total characters601614
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1000-1500
2nd row1000-1500
3rd row1000-1500
4th row1000-1500
5th row2500 or more, or no clouds.

Common Values

ValueCountFrequency (%)
600-1000 22645
34.6%
2500 or more, or no clouds. 9942
15.2%
300-600 8941
 
13.6%
1000-1500 6162
 
9.4%
200-300 3547
 
5.4%
100-200 743
 
1.1%
1500-2000 344
 
0.5%
2000-2500 76
 
0.1%
50-100 21
 
< 0.1%
Less than 50 3
 
< 0.1%
(Missing) 13086
20.0%

Length

2024-06-23T18:19:47.589683image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T18:19:47.837949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
600-1000 22645
22.2%
or 19884
19.5%
2500 9942
9.7%
more 9942
9.7%
no 9942
9.7%
clouds 9942
9.7%
300-600 8941
 
8.8%
1000-1500 6162
 
6.0%
200-300 3547
 
3.5%
100-200 743
 
0.7%
Other values (6) 450
 
0.4%

Most occurring characters

ValueCountFrequency (%)
0 219009
36.4%
49719
 
8.3%
o 49710
 
8.3%
- 42479
 
7.1%
1 36077
 
6.0%
6 31586
 
5.3%
r 29826
 
5.0%
5 16548
 
2.8%
2 14728
 
2.4%
3 12488
 
2.1%
Other values (14) 99444
16.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 601614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 219009
36.4%
49719
 
8.3%
o 49710
 
8.3%
- 42479
 
7.1%
1 36077
 
6.0%
6 31586
 
5.3%
r 29826
 
5.0%
5 16548
 
2.8%
2 14728
 
2.4%
3 12488
 
2.1%
Other values (14) 99444
16.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 601614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 219009
36.4%
49719
 
8.3%
o 49710
 
8.3%
- 42479
 
7.1%
1 36077
 
6.0%
6 31586
 
5.3%
r 29826
 
5.0%
5 16548
 
2.8%
2 14728
 
2.4%
3 12488
 
2.1%
Other values (14) 99444
16.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 601614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 219009
36.4%
49719
 
8.3%
o 49710
 
8.3%
- 42479
 
7.1%
1 36077
 
6.0%
6 31586
 
5.3%
r 29826
 
5.0%
5 16548
 
2.8%
2 14728
 
2.4%
3 12488
 
2.1%
Other values (14) 99444
16.5%

Cm
Categorical

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing33715
Missing (%)51.5%
Memory size511.9 KiB
No Altocumulus, Altostratus or Nimbostratus.
13133 
Altocumulus castellanus or floccus.
5997 
Altocumulus translucidus at a single level.
5884 
Altostratus opacus or Nimbostratus.
2698 
Altostratus translucidus.
1598 
Other values (5)
2485 

Length

Max length183
Median length182
Mean length48.345023
Min length25

Characters and Unicode

Total characters1537130
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNo Altocumulus, Altostratus or Nimbostratus.
2nd rowNo Altocumulus, Altostratus or Nimbostratus.
3rd rowNo Altocumulus, Altostratus or Nimbostratus.
4th rowNo Altocumulus, Altostratus or Nimbostratus.
5th rowNo Altocumulus, Altostratus or Nimbostratus.

Common Values

ValueCountFrequency (%)
No Altocumulus, Altostratus or Nimbostratus. 13133
 
20.0%
Altocumulus castellanus or floccus. 5997
 
9.2%
Altocumulus translucidus at a single level. 5884
 
9.0%
Altostratus opacus or Nimbostratus. 2698
 
4.1%
Altostratus translucidus. 1598
 
2.4%
Altocumulus translucidus or opacus in two or more layers, or Altocumulus opacus in a single layer, not progressively invading the sky, or Altocumulus with Altostratus or Nimbostratus. 940
 
1.4%
Altocumulus translucidus in bands, or one or more layers of Altocumulus translucidus or opacus, progressively invading the sky; these Altocumulus clouds generally thicken as a whole. 674
 
1.0%
Altocumulus cumulogenitus (or cumulonimbogenitus). 480
 
0.7%
Patches (often lenticular) of Altocumulus translucidus, continually changing and occurring at one or more levels. 375
 
0.6%
Altocumulus of a chaotic sky, generally at several levels. 16
 
< 0.1%
(Missing) 33715
51.5%

Length

2024-06-23T18:19:48.178184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T18:19:48.476695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
altocumulus 30727
16.2%
or 29405
15.5%
altostratus 18369
9.7%
nimbostratus 16771
 
8.8%
no 13133
 
6.9%
translucidus 10145
 
5.4%
a 7514
 
4.0%
single 6824
 
3.6%
at 6275
 
3.3%
castellanus 5997
 
3.2%
Other values (35) 44378
23.4%

Most occurring characters

ValueCountFrequency (%)
u 169536
11.0%
s 162274
10.6%
157743
10.3%
t 150051
9.8%
l 136705
8.9%
o 131180
 
8.5%
a 84433
 
5.5%
r 84292
 
5.5%
c 68705
 
4.5%
m 50927
 
3.3%
Other values (21) 341284
22.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1537130
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 169536
11.0%
s 162274
10.6%
157743
10.3%
t 150051
9.8%
l 136705
8.9%
o 131180
 
8.5%
a 84433
 
5.5%
r 84292
 
5.5%
c 68705
 
4.5%
m 50927
 
3.3%
Other values (21) 341284
22.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1537130
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 169536
11.0%
s 162274
10.6%
157743
10.3%
t 150051
9.8%
l 136705
8.9%
o 131180
 
8.5%
a 84433
 
5.5%
r 84292
 
5.5%
c 68705
 
4.5%
m 50927
 
3.3%
Other values (21) 341284
22.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1537130
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 169536
11.0%
s 162274
10.6%
157743
10.3%
t 150051
9.8%
l 136705
8.9%
o 131180
 
8.5%
a 84433
 
5.5%
r 84292
 
5.5%
c 68705
 
4.5%
m 50927
 
3.3%
Other values (21) 341284
22.2%

Ch
Categorical

MISSING 

Distinct10
Distinct (%)< 0.1%
Missing37018
Missing (%)56.5%
Memory size511.9 KiB
No Cirrus, Cirrocumulus or Cirrostratus.
11179 
Cirrocumulus alone, or Cirrocumulus accompanied by Cirrus or Cirrostratus or both, but Cirrocumulus is predominant.
8182 
Cirrus fibratus, sometimes uncinus, not progressively invading the sky.
6471 
Cirrus (often in bands) and Cirrostratus, or Cirrostratus alone, progressively invading the sky; they generally thicken as a whole; the continuous veil extends more than 45 degrees above the horizon, without the sky being totally covered.
 
661
Cirrus spissatus, in patches or entangled sheaves, which usually do not increase and sometimes seem to be the remains of the upper part of a Cumulonimbus; or Cirrus castellanus or floccus.
 
645
Other values (5)
1354 

Length

Max length238
Median length200
Mean length80.704198
Min length36

Characters and Unicode

Total characters2299424
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCirrus fibratus, sometimes uncinus, not progressively invading the sky.
2nd rowCirrus fibratus, sometimes uncinus, not progressively invading the sky.
3rd rowCirrus fibratus, sometimes uncinus, not progressively invading the sky.
4th rowCirrostratus covering the whole sky.
5th rowCirrus (often in bands) and Cirrostratus, or Cirrostratus alone, progressively invading the sky; they generally thicken as a whole; the continuous veil extends more than 45 degrees above the horizon, without the sky being totally covered.

Common Values

ValueCountFrequency (%)
No Cirrus, Cirrocumulus or Cirrostratus. 11179
 
17.1%
Cirrocumulus alone, or Cirrocumulus accompanied by Cirrus or Cirrostratus or both, but Cirrocumulus is predominant. 8182
 
12.5%
Cirrus fibratus, sometimes uncinus, not progressively invading the sky. 6471
 
9.9%
Cirrus (often in bands) and Cirrostratus, or Cirrostratus alone, progressively invading the sky; they generally thicken as a whole; the continuous veil extends more than 45 degrees above the horizon, without the sky being totally covered. 661
 
1.0%
Cirrus spissatus, in patches or entangled sheaves, which usually do not increase and sometimes seem to be the remains of the upper part of a Cumulonimbus; or Cirrus castellanus or floccus. 645
 
1.0%
Cirrus (often in bands) and Cirrostratus, or Cirrostratus alone, progressively invading the sky; they generally thicken as a whole, but the continuous veil does not reach 45 degrees above the horizon. 566
 
0.9%
Cirrus uncinus or fibratus, or both, progressively invading the sky; they generally thicken as a whole. 431
 
0.7%
Cirrostratus covering the whole sky. 216
 
0.3%
Cirrus spissatus cumulonimbogenitus. 71
 
0.1%
Cirrostratus not progressively invading the sky and not entirely covering it. 70
 
0.1%
(Missing) 37018
56.5%

Length

2024-06-23T18:19:48.932668image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T18:19:49.250273image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
or 39749
 
12.9%
cirrocumulus 35725
 
11.6%
cirrus 28851
 
9.4%
cirrostratus 22101
 
7.2%
the 12820
 
4.2%
no 11179
 
3.6%
alone 9409
 
3.1%
sky 9076
 
3.0%
but 8748
 
2.8%
both 8613
 
2.8%
Other values (57) 120889
39.4%

Most occurring characters

ValueCountFrequency (%)
278668
12.1%
r 275622
12.0%
u 196785
 
8.6%
s 187066
 
8.1%
o 183021
 
8.0%
i 169137
 
7.4%
t 118293
 
5.1%
e 99071
 
4.3%
n 91595
 
4.0%
a 89581
 
3.9%
Other values (24) 610585
26.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2299424
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
278668
12.1%
r 275622
12.0%
u 196785
 
8.6%
s 187066
 
8.1%
o 183021
 
8.0%
i 169137
 
7.4%
t 118293
 
5.1%
e 99071
 
4.3%
n 91595
 
4.0%
a 89581
 
3.9%
Other values (24) 610585
26.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2299424
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
278668
12.1%
r 275622
12.0%
u 196785
 
8.6%
s 187066
 
8.1%
o 183021
 
8.0%
i 169137
 
7.4%
t 118293
 
5.1%
e 99071
 
4.3%
n 91595
 
4.0%
a 89581
 
3.9%
Other values (24) 610585
26.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2299424
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
278668
12.1%
r 275622
12.0%
u 196785
 
8.6%
s 187066
 
8.1%
o 183021
 
8.0%
i 169137
 
7.4%
t 118293
 
5.1%
e 99071
 
4.3%
n 91595
 
4.0%
a 89581
 
3.9%
Other values (24) 610585
26.6%

VV
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing25905
Missing (%)39.5%
Memory size511.9 KiB

Td
Real number (ℝ)

Distinct539
Distinct (%)0.8%
Missing36
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1.9512326
Minimum-33.3
Maximum23.3
Zeros365
Zeros (%)0.6%
Negative27596
Negative (%)42.1%
Memory size511.9 KiB
2024-06-23T18:19:49.725147image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-33.3
5-th percentile-14.4
Q1-4.4
median1.7
Q39.8
95-th percentile16
Maximum23.3
Range56.6
Interquartile range (IQR)14.2

Descriptive statistics

Standard deviation9.396289
Coefficient of variation (CV)4.8155659
Kurtosis-0.38294005
Mean1.9512326
Median Absolute Deviation (MAD)7.1
Skewness-0.32732885
Sum127755
Variance88.290246
MonotonicityNot monotonic
2024-06-23T18:19:50.029814image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 365
 
0.6%
-0.2 360
 
0.5%
0.2 350
 
0.5%
0.3 339
 
0.5%
0.4 337
 
0.5%
0.1 335
 
0.5%
-0.5 328
 
0.5%
-0.3 326
 
0.5%
0.6 318
 
0.5%
-1.4 313
 
0.5%
Other values (529) 62103
94.8%
ValueCountFrequency (%)
-33.3 1
 
< 0.1%
-32.8 1
 
< 0.1%
-32.7 2
< 0.1%
-32.5 2
< 0.1%
-32.4 1
 
< 0.1%
-32.3 2
< 0.1%
-32.1 1
 
< 0.1%
-31.9 1
 
< 0.1%
-31.3 3
< 0.1%
-31.1 1
 
< 0.1%
ValueCountFrequency (%)
23.3 1
 
< 0.1%
22.9 1
 
< 0.1%
22.8 1
 
< 0.1%
22.7 1
 
< 0.1%
22.5 1
 
< 0.1%
22.1 2
< 0.1%
22 1
 
< 0.1%
21.9 3
< 0.1%
21.8 4
< 0.1%
21.7 1
 
< 0.1%

RRR
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing44894
Missing (%)68.5%
Memory size511.9 KiB

tR
Categorical

IMBALANCE  MISSING 

Distinct3
Distinct (%)< 0.1%
Missing44893
Missing (%)68.5%
Memory size511.9 KiB
12.0
20615 
24.0
 
1
2.0
 
1

Length

Max length4
Median length4
Mean length3.9999515
Min length3

Characters and Unicode

Total characters82467
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row12.0
2nd row12.0
3rd row12.0
4th row12.0
5th row12.0

Common Values

ValueCountFrequency (%)
12.0 20615
31.5%
24.0 1
 
< 0.1%
2.0 1
 
< 0.1%
(Missing) 44893
68.5%

Length

2024-06-23T18:19:50.322448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T18:19:50.546621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
12.0 20615
> 99.9%
24.0 1
 
< 0.1%
2.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 20617
25.0%
. 20617
25.0%
0 20617
25.0%
1 20615
25.0%
4 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 82467
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 20617
25.0%
. 20617
25.0%
0 20617
25.0%
1 20615
25.0%
4 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 82467
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 20617
25.0%
. 20617
25.0%
0 20617
25.0%
1 20615
25.0%
4 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 82467
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 20617
25.0%
. 20617
25.0%
0 20617
25.0%
1 20615
25.0%
4 1
 
< 0.1%

E
Categorical

MISSING 

Distinct6
Distinct (%)0.2%
Missing62595
Missing (%)95.6%
Memory size511.9 KiB
Surface of ground moist.
1334 
Surface of ground dry (without cracks and no appreciable amount of dust or loose sand).
741 
Surface of ground wet (standing water in small or large pools on surface).
601 
Flooded.
142 
Surface of ground frozen.
 
96

Length

Max length87
Median length74
Mean length49.587307
Min length8

Characters and Unicode

Total characters144547
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowSurface of ground wet (standing water in small or large pools on surface).
2nd rowSurface of ground wet (standing water in small or large pools on surface).
3rd rowSurface of ground wet (standing water in small or large pools on surface).
4th rowFlooded.
5th rowFlooded.

Common Values

ValueCountFrequency (%)
Surface of ground moist. 1334
 
2.0%
Surface of ground dry (without cracks and no appreciable amount of dust or loose sand). 741
 
1.1%
Surface of ground wet (standing water in small or large pools on surface). 601
 
0.9%
Flooded. 142
 
0.2%
Surface of ground frozen. 96
 
0.1%
Loose dry dust or sand not covering ground completely. 1
 
< 0.1%
(Missing) 62595
95.6%

Length

2024-06-23T18:19:50.814893image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T18:19:51.075610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
of 3513
14.2%
surface 3373
 
13.6%
ground 2773
 
11.2%
or 1343
 
5.4%
moist 1334
 
5.4%
dust 742
 
3.0%
loose 742
 
3.0%
sand 742
 
3.0%
dry 742
 
3.0%
no 741
 
3.0%
Other values (18) 8754
35.3%

Most occurring characters

ValueCountFrequency (%)
21884
15.1%
o 14856
 
10.3%
r 11012
 
7.6%
a 10224
 
7.1%
u 8370
 
5.8%
n 8240
 
5.7%
e 7641
 
5.3%
f 6982
 
4.8%
s 6705
 
4.6%
d 6625
 
4.6%
Other values (20) 42008
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 144547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
21884
15.1%
o 14856
 
10.3%
r 11012
 
7.6%
a 10224
 
7.1%
u 8370
 
5.8%
n 8240
 
5.7%
e 7641
 
5.3%
f 6982
 
4.8%
s 6705
 
4.6%
d 6625
 
4.6%
Other values (20) 42008
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 144547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
21884
15.1%
o 14856
 
10.3%
r 11012
 
7.6%
a 10224
 
7.1%
u 8370
 
5.8%
n 8240
 
5.7%
e 7641
 
5.3%
f 6982
 
4.8%
s 6705
 
4.6%
d 6625
 
4.6%
Other values (20) 42008
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 144547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
21884
15.1%
o 14856
 
10.3%
r 11012
 
7.6%
a 10224
 
7.1%
u 8370
 
5.8%
n 8240
 
5.7%
e 7641
 
5.3%
f 6982
 
4.8%
s 6705
 
4.6%
d 6625
 
4.6%
Other values (20) 42008
29.1%

Tg
Real number (ℝ)

MISSING 

Distinct35
Distinct (%)1.5%
Missing63212
Missing (%)96.5%
Infinite0
Infinite (%)0.0%
Mean9.4577894
Minimum-11
Maximum25
Zeros87
Zeros (%)0.1%
Negative105
Negative (%)0.2%
Memory size511.9 KiB
2024-06-23T18:19:51.367990image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-11
5-th percentile0
Q15
median10
Q314
95-th percentile18
Maximum25
Range36
Interquartile range (IQR)9

Descriptive statistics

Standard deviation5.7974972
Coefficient of variation (CV)0.61298649
Kurtosis-0.65781667
Mean9.4577894
Median Absolute Deviation (MAD)4
Skewness-0.29804051
Sum21734
Variance33.610973
MonotonicityNot monotonic
2024-06-23T18:19:51.652937image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
12 156
 
0.2%
14 146
 
0.2%
15 145
 
0.2%
13 143
 
0.2%
10 135
 
0.2%
11 131
 
0.2%
16 131
 
0.2%
6 117
 
0.2%
8 114
 
0.2%
7 114
 
0.2%
Other values (25) 966
 
1.5%
(Missing) 63212
96.5%
ValueCountFrequency (%)
-11 1
 
< 0.1%
-10 1
 
< 0.1%
-8 4
 
< 0.1%
-7 1
 
< 0.1%
-6 2
 
< 0.1%
-5 3
 
< 0.1%
-4 4
 
< 0.1%
-3 9
 
< 0.1%
-2 24
< 0.1%
-1 56
0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
23 1
 
< 0.1%
22 6
 
< 0.1%
21 1
 
< 0.1%
20 22
 
< 0.1%
19 43
 
0.1%
18 72
0.1%
17 96
0.1%
16 131
0.2%
15 145
0.2%

E'
Categorical

MISSING 

Distinct7
Distinct (%)0.3%
Missing62818
Missing (%)95.9%
Memory size511.9 KiB
Even layer of loose dry snow covering ground completely.
1246 
Even layer of compact or wet snow covering ground completely.
1008 
Compact or wet snow (with or without ice) covering less than one-half of the ground.
272 
Compact or wet snow (with or without ice) covering at least one-half of the ground but ground not completely covered.
134 
Ground predominantly covered by ice.
 
29
Other values (2)
 
3

Length

Max length117
Median length90
Mean length63.54792
Min length36

Characters and Unicode

Total characters171071
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowCompact or wet snow (with or without ice) covering at least one-half of the ground but ground not completely covered.
2nd rowCompact or wet snow (with or without ice) covering at least one-half of the ground but ground not completely covered.
3rd rowCompact or wet snow (with or without ice) covering at least one-half of the ground but ground not completely covered.
4th rowEven layer of compact or wet snow covering ground completely.
5th rowEven layer of compact or wet snow covering ground completely.

Common Values

ValueCountFrequency (%)
Even layer of loose dry snow covering ground completely. 1246
 
1.9%
Even layer of compact or wet snow covering ground completely. 1008
 
1.5%
Compact or wet snow (with or without ice) covering less than one-half of the ground. 272
 
0.4%
Compact or wet snow (with or without ice) covering at least one-half of the ground but ground not completely covered. 134
 
0.2%
Ground predominantly covered by ice. 29
 
< 0.1%
Loose dry snow covering at least one-half of the ground but ground not completely covered. 2
 
< 0.1%
Loose dry snow covering less than one-half of the ground. 1
 
< 0.1%
(Missing) 62818
95.9%

Length

2024-06-23T18:19:51.969442image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-06-23T18:19:52.252124image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
ground 2828
10.0%
of 2663
9.4%
snow 2663
9.4%
covering 2663
9.4%
completely 2390
8.5%
even 2254
 
8.0%
layer 2254
 
8.0%
or 1820
 
6.4%
compact 1414
 
5.0%
wet 1414
 
5.0%
Other values (16) 5876
20.8%

Most occurring characters

ValueCountFrequency (%)
25547
14.9%
o 20084
11.7%
e 16635
 
9.7%
n 11284
 
6.6%
r 11008
 
6.4%
l 9127
 
5.3%
c 8075
 
4.7%
t 7691
 
4.5%
y 5951
 
3.5%
g 5462
 
3.2%
Other values (20) 50207
29.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 171071
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
25547
14.9%
o 20084
11.7%
e 16635
 
9.7%
n 11284
 
6.6%
r 11008
 
6.4%
l 9127
 
5.3%
c 8075
 
4.7%
t 7691
 
4.5%
y 5951
 
3.5%
g 5462
 
3.2%
Other values (20) 50207
29.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 171071
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
25547
14.9%
o 20084
11.7%
e 16635
 
9.7%
n 11284
 
6.6%
r 11008
 
6.4%
l 9127
 
5.3%
c 8075
 
4.7%
t 7691
 
4.5%
y 5951
 
3.5%
g 5462
 
3.2%
Other values (20) 50207
29.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 171071
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
25547
14.9%
o 20084
11.7%
e 16635
 
9.7%
n 11284
 
6.6%
r 11008
 
6.4%
l 9127
 
5.3%
c 8075
 
4.7%
t 7691
 
4.5%
y 5951
 
3.5%
g 5462
 
3.2%
Other values (20) 50207
29.3%

sss
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing63042
Missing (%)96.2%
Memory size511.9 KiB

Interactions

2024-06-23T18:19:30.220160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:02.078661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:04.969606image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:07.840841image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:10.702484image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:13.221212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:15.691422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:18.318226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:20.577015image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:22.981625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:25.516626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:27.745888image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:30.411716image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:02.391428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:05.207747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:08.073275image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:10.911737image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:13.428818image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:15.918734image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:18.493839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:20.864979image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:23.181595image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:25.725661image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:27.954541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:30.611800image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:02.626626image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:05.468503image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:08.317875image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:11.127530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:13.647820image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:16.139611image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:18.678804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:21.060980image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:23.393196image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:25.925625image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:28.168110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:30.824985image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:02.876621image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:05.685501image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:08.687110image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:11.335534image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:13.842141image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:16.349610image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:18.859837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:21.247981image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:23.631315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:26.125623image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:28.384889image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:31.042691image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:03.165622image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:05.879785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:08.923112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:11.571396image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:14.025322image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:16.539224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:19.030809image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:21.454706image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:23.836456image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:26.305629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:28.573129image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:31.254789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:03.407765image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:06.100802image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:09.155785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:11.761206image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:14.205359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:16.890576image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:19.253838image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:21.640789image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:24.049797image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:26.520251image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:28.783730image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:31.451430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:03.693345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:06.333404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:09.386375image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:11.966428image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:14.407608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:17.111607image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:19.442510image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:21.855404image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:24.276131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:26.710281image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:29.013437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:31.646168image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:03.926346image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:06.538012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:09.602374image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:12.156121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:14.608608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:17.304608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:19.648479image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:22.049862image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:24.444695image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:26.870430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:29.218746image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:31.821839image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:04.146946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:06.735643image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:09.809524image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:12.404955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:14.799223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:17.502254image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:19.849514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:22.251355image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:24.760901image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:27.033276image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:29.409026image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:32.041483image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:04.343946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:07.058528image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:10.033686image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:12.634095image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:15.069193image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:17.706226image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:20.018511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:22.442884image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:24.954870image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:27.199736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:29.607020image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:32.187139image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:04.551608image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:07.316052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:10.259721image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:12.820543image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:15.277223image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:17.903259image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:20.173293image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:22.603738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:25.110997image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:27.391414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:29.818131image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:32.395788image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:04.749580image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:07.613671image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:10.469967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:13.001560image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:15.460900image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:18.117224image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:20.381009image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:22.786785image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:25.310001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:27.585713image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-06-23T18:19:30.010319image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-06-23T18:19:32.990761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-23T18:19:33.899200image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-06-23T18:19:35.201385image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Local time in MoscowTPoPPaUDDFfff10ff3NWWW1W2TnTxClNhHCmChVVTdRRRtRETgE'sss
025.04.2024 21:0014.9744.0757.30.969.0Calm, no wind0.0NaNNaN70 – 80%.NaNNaNNaN21.5Stratocumulus cumulogenitus.20–30%.1000-1500No Altocumulus, Altostratus or Nimbostratus.Cirrus fibratus, sometimes uncinus, not progressively invading the sky.209.2No precipitation12.0NaNNaNNaNNaN
125.04.2024 18:0020.6743.1756.10.246.0Wind blowing from the south-southeast1.0NaNNaN70 – 80%.NaNNaNNaNNaNCumulus humilis or Cumulus fractus other than of bad weather, or both.40%.1000-1500No Altocumulus, Altostratus or Nimbostratus.Cirrus fibratus, sometimes uncinus, not progressively invading the sky.208.6No precipitation12.0NaNNaNNaNNaN
225.04.2024 15:0020.2742.9755.80.153.0Wind blowing from the south-east2.0NaNNaN90 or more, but not 100%NaNNaNNaNNaNCumulus mediocris or congestus, with or without Cumulus of species fractus or humilis or Stratocumulus, all having their bases at the same level.60%.1000-1500No Altocumulus, Altostratus or Nimbostratus.Cirrus fibratus, sometimes uncinus, not progressively invading the sky.2010.2NaNNaNNaNNaNNaNNaN
325.04.2024 12:0018.1742.8755.80.155.0Wind blowing from the south-southeast2.0NaNNaN100%.NaNNaNNaNNaNStratocumulus other than Stratocumulus cumulogenitus.60%.1000-1500No Altocumulus, Altostratus or Nimbostratus.Cirrostratus covering the whole sky.198.9NaNNaNNaNNaNNaNNaN
425.04.2024 09:0014.1742.7756.1-1.158.0Wind blowing from the south-southeast3.0NaNNaN100%.NaNNaN10.2NaNNo Stratocumulus, Stratus, Cumulus or Cumulonimbus.no clouds2500 or more, or no clouds.No Altocumulus, Altostratus or Nimbostratus.Cirrus (often in bands) and Cirrostratus, or Cirrostratus alone, progressively invading the sky; they generally thicken as a whole; the continuous veil extends more than 45 degrees above the horizon, without the sky being totally covered.205.80.112.0Surface of ground wet (standing water in small or large pools on surface).8.0NaNNaN
525.04.2024 06:0011.0743.8757.3-2.462.0Wind blowing from the south-east2.0NaNNaN60%.NaNNaN10.2NaNStratocumulus other than Stratocumulus cumulogenitus.60%.1000-1500No Altocumulus, Altostratus or Nimbostratus.No Cirrus, Cirrocumulus or Cirrostratus.204.00.112.0NaNNaNNaNNaN
625.04.2024 03:0010.4746.2759.7-1.764.0Wind blowing from the east2.0NaNNaN100%.Shower(s) of rain.Shower(s).Cloud covering more than 1/2 of the sky throughout the appropriate period.NaNNaNStratocumulus other than Stratocumulus cumulogenitus.100%.1000-1500NaNNaN203.9NaNNaNNaNNaNNaNNaN
725.04.2024 00:0011.5747.9761.4-1.150.0Wind blowing from the east-southeast1.0NaNNaN100%.NaNNaNNaNNaNNo Stratocumulus, Stratus, Cumulus or Cumulonimbus.100%.2500 or more, or no clouds.Altocumulus translucidus in bands, or one or more layers of Altocumulus translucidus or opacus, progressively invading the sky; these Altocumulus clouds generally thicken as a whole.NaN201.3NaNNaNNaNNaNNaNNaN
824.04.2024 21:0012.2749.0762.5-0.945.0Wind blowing from the east-southeast2.0NaNNaN90 or more, but not 100%NaNNaNNaN13.9No Stratocumulus, Stratus, Cumulus or Cumulonimbus.90 or more, but not 100%2500 or more, or no clouds.Altocumulus translucidus at a single level.No Cirrus, Cirrocumulus or Cirrostratus.200.4No precipitation12.0NaNNaNNaNNaN
924.04.2024 18:0013.2749.9763.4-1.637.0Wind blowing from the east-southeast1.0NaNNaN70 – 80%.NaNNaNNaNNaNNo Stratocumulus, Stratus, Cumulus or Cumulonimbus.60%.2500 or more, or no clouds.Altocumulus translucidus at a single level.Cirrus fibratus, sometimes uncinus, not progressively invading the sky.20-1.4No precipitation12.0NaNNaNNaNNaN
Local time in MoscowTPoPPaUDDFfff10ff3NWWW1W2TnTxClNhHCmChVVTdRRRtRETgE'sss
6550002.02.2005 06:00-10.1753.3768.7NaN85.0Wind blowing from the south-east2.0NaNNaN90 or more, but not 100%Continuous fall of snowflakes, slight at time of observation.Snow, or rain and snow mixed.Cloud covering more than 1/2 of the sky throughout the appropriate period.NaNNaNStratocumulus other than Stratocumulus cumulogenitus.90 or more, but not 100%600-1000NaNNaNNaN-12.10.412.0NaNNaNNaNNaN
6550102.02.2005 03:00-9.3752.2767.6NaN82.0Wind blowing from the south-east2.0NaNNaN90 or more, but not 100%State of sky on the whole unchanged.Snow, or rain and snow mixed.Cloud covering more than 1/2 of the sky throughout the appropriate period.NaNNaNStratocumulus other than Stratocumulus cumulogenitus.90 or more, but not 100%600-1000NaNNaNNaN-11.8NaNNaNNaNNaNNaNNaN
6550202.02.2005 00:00-9.1751.0766.3NaN85.0Wind blowing from the south-east2.0NaNNaN100%.Continuous fall of snowflakes, slight at time of observation.Snow, or rain and snow mixed.Cloud covering more than 1/2 of the sky throughout the appropriate period.NaNNaNStratus fractus or Cumulus fractus of bad weather, or both (pannus), usually below Altostratus or Nimbostratus.70 – 80%.300-600NaNCirrocumulus alone, or Cirrocumulus accompanied by Cirrus or Cirrostratus or both, but Cirrocumulus is predominant.NaN-11.1NaNNaNNaNNaNNaNNaN
6550301.02.2005 21:00-8.9750.0765.3NaN83.0Wind blowing from the south-east2.0NaNNaN90 or more, but not 100%Continuous fall of snowflakes, slight at time of observation.Shower(s).Snow, or rain and snow mixed.NaN-6.4Stratocumulus other than Stratocumulus cumulogenitus.90 or more, but not 100%600-1000NaNNaNNaN-11.3112.0NaNNaNNaNNaN
6550401.02.2005 18:00-7.9748.7764.0NaN87.0Wind blowing from the east-southeast2.0NaNNaN90 or more, but not 100%Continuous fall of snowflakes, slight at time of observation.Shower(s).Snow, or rain and snow mixed.NaNNaNStratocumulus other than Stratocumulus cumulogenitus.90 or more, but not 100%600-1000NaNNaNNaN-9.7NaNNaNNaNNaNNaNNaN
6550501.02.2005 15:00-6.6746.8761.8NaN83.0Wind blowing from the south-east3.0NaNNaN100%.Snow shower(s), slight.Shower(s).Snow, or rain and snow mixed.NaNNaNCumulus mediocris or congestus, with or without Cumulus of species fractus or humilis or Stratocumulus, all having their bases at the same level.100%.300-600Altocumulus castellanus or floccus.Cirrocumulus alone, or Cirrocumulus accompanied by Cirrus or Cirrostratus or both, but Cirrocumulus is predominant.4-9.0NaNNaNNaNNaNNaNNaN
6550601.02.2005 12:00-7.1745.0760.0NaN85.0Wind blowing from the south-east3.0NaNNaN90 or more, but not 100%Snow not falling as shower(s).Snow, or rain and snow mixed.Sandstorm, duststorm or blowing snow.NaNNaNStratocumulus other than Stratocumulus cumulogenitus.90 or more, but not 100%600-1000NaNNaN10-9.2NaNNaNNaNNaNNaNNaN
6550701.02.2005 09:00-8.6743.5758.6NaN89.0Wind blowing from the south-east3.0NaNNaN100%.Continuous fall of snowflakes, moderate at time of observation.Snow, or rain and snow mixed.Sandstorm, duststorm or blowing snow.-9.4NaNStratus fractus or Cumulus fractus of bad weather, or both (pannus), usually below Altostratus or Nimbostratus.100%.300-600Altostratus translucidus.Cirrocumulus alone, or Cirrocumulus accompanied by Cirrus or Cirrostratus or both, but Cirrocumulus is predominant.4-10.1312.0NaNNaNEven layer of loose dry snow covering ground completely.43
6550801.02.2005 06:00-8.2742.8757.9NaN90.0Wind blowing from the south-east3.0NaNNaN100%.Continuous fall of snowflakes, slight at time of observation.Snow, or rain and snow mixed.Cloud covering more than 1/2 of the sky throughout the appropriate period.NaNNaNStratus fractus or Cumulus fractus of bad weather, or both (pannus), usually below Altostratus or Nimbostratus.70 – 80%.300-600Altostratus translucidus.Cirrocumulus alone, or Cirrocumulus accompanied by Cirrus or Cirrostratus or both, but Cirrocumulus is predominant.NaN-9.6212.0NaNNaNNaNNaN
6550901.02.2005 03:00-8.6743.0758.1NaN89.0Wind blowing from the south-east2.0NaNNaN100%.Continuous fall of snowflakes, slight at time of observation.Snow, or rain and snow mixed.Cloud covering more than 1/2 of the sky throughout the appropriate period.NaNNaNStratocumulus other than Stratocumulus cumulogenitus.70 – 80%.600-1000Altostratus translucidus.Cirrocumulus alone, or Cirrocumulus accompanied by Cirrus or Cirrostratus or both, but Cirrocumulus is predominant.NaN-10.1NaNNaNNaNNaNNaNNaN